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Proceedings Paper

Research on the technique of public watermarking system based on wavelet transform and neural network
Author(s): Li Xu; Gu Tao
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Paper Abstract

A hybrid algorithm of using a wavelet transform and a neural network is presented which solves the problems confronted in public watermarking systems. First, to get the wavelet coefficients, db1 wavelet is used to decompose the selected image. Second, to ensure better quality of the watermarked image, some wavelet coefficients and their closely relevant wavelet coefficients are randomly selected from the wavelet coefficients decomposed by the low pass filter and used to establish the relational model by using a neural network. Third, the bit information of the watermark is also enlarged by increasing the amount of zeros or ones and then one bit of the results is embedded by adjusting the polarity between a chosen wavelet coefficient and the output value of the model. Finally, a new image with watermark information is reconstructed by using the modified wavelet coefficients and other unmodified wavelet coefficients. On the other hand, the process of retrieving the watermark is the inverse of the embedding process. The embedded watermark can also be retrieved by using the hybrid algorithm and the restore function without knowing the original image and watermark. Experimental results show that the proposed technique is very robust against some image processing operations and JPEG lossy compression. Meanwhile, the extracted watermark can be proved by the proposed method. Because of the neural network, the proposed method is also robust against attack of false authentication. Therefore, the hybrid algorithm can be used to protect the copyright of one important image.

Paper Details

Date Published: 9 April 2007
PDF: 7 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65761B (9 April 2007); doi: 10.1117/12.717879
Show Author Affiliations
Li Xu, North China Institute of Science and Technology (China)
Gu Tao, North China Institute of Science and Technology (China)

Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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